{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\", style=\"color:green\">Matplotlib Tutorial: Histogram</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**In histograms, x axis contains a variable and y axis will be a frequency of that variable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We have a sample data of blood sugar level of different patients, we will try to plot number of patients by blood range and try to figure out how many patients are normal, pre-diabetic and diabetic**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 3.,  3.,  1.,  0.,  1.,  1.,  0.,  2.,  0.,  2.]),\n",
       " array([  77. ,   84.3,   91.6,   98.9,  106.2,  113.5,  120.8,  128.1,\n",
       "         135.4,  142.7,  150. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng/4XpsI3w0L+pV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c74035f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blood_sugar = [113, 85, 90, 150, 149, 88, 93, 115, 135, 80, 77, 82, 129]\n",
    "plt.hist(blood_sugar, rwidth=0.8) # by default number of bins is set to 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>bins parameter</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 7.,  1.,  2.,  3.]),\n",
       " array([  77.  ,   95.25,  113.5 ,  131.75,  150.  ]),\n",
       " <a list of 4 Patch objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njMUtSc1Y3JLUjMUtSc38P/2xB6N+kLfOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c7400ca780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(blood_sugar,rwidth=0.5,bins=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Histogram showing normal, pre-diabetic and diabetic patients distribution</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul style=\"color:brown;font-weight: bold;\">\n",
    "    <li>80-100: Normal</li>\n",
    "    <li>100-125: Pre-diabetic</li>\n",
    "    <li>80-100: Diabetic</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6.,  2.,  4.]),\n",
       " array([ 80, 100, 125, 150]),\n",
       " <a list of 3 Patch objects>)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEWCAYAAABliCz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c74186e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Sugar Level\")\n",
    "plt.ylabel(\"Number Of Patients\")\n",
    "plt.title(\"Blood Sugar Chart\")\n",
    "\n",
    "plt.hist(blood_sugar, bins=[80,100,125,150], rwidth=0.95, color='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Mutiple data samples in a histogram</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c741b46be0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEWCAYAAABliCz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nN+4WpoJK7eEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c741a09f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Sugar Level\")\n",
    "plt.ylabel(\"Number Of Patients\")\n",
    "plt.title(\"Blood Sugar Chart\")\n",
    "\n",
    "blood_sugar_men = [113, 85, 90, 150, 149, 88, 93, 115, 135, 80, 77, 82, 129]\n",
    "blood_sugar_women = [67, 98, 89, 120, 133, 150, 84, 69, 89, 79, 120, 112, 100]\n",
    "\n",
    "plt.hist([blood_sugar_men,blood_sugar_women], bins=[80,100,125,150], rwidth=0.95, color=['green','orange'],label=['men','women'])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>histtype=step</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6.,  2.,  4.]),\n",
       " array([ 80, 100, 125, 150]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEWCAYAAABliCz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c7419709b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Sugar Level\")\n",
    "plt.ylabel(\"Number Of Patients\")\n",
    "plt.title(\"Blood Sugar Chart\")\n",
    "\n",
    "plt.hist(blood_sugar,bins=[80,100,125,150],rwidth=0.95,histtype='step')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>horizontal orientation</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 5.,  2.,  3.]),\n",
       " array([ 80, 100, 125, 150]),\n",
       " <a list of 3 Patch objects>)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIha/wf2WOuJ8AeflQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c73ec0de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Number Of Patients\")\n",
    "plt.ylabel(\"Sugar Level\")\n",
    "plt.title(\"Blood Sugar Chart\")\n",
    "\n",
    "plt.hist(blood_sugar, bins=[80,100,125,150], rwidth=0.95, orientation='horizontal')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
